Parallel and Distributed Processing and Applications

Lecture Notes in Computer Science, 2006

Parallel computing is strongly influenced by the challenges of distributed systems, such as a need for a Single System Image, resource sharing and allocation, failures and a need for fault tolerance, long latencies, network partition, disconnected operation, demands of users wishing to solve more computationally and communication demanding problems, and opportunities created by grids and Web services. Distributed computing is the computing mainstream now; it is based on different forms of distributed systems: clusters, grids, peer-to-peer systems, web services, service oriented architectures. This topic provides a forum for research and practice, of interest to both academia and industry, about distributed computing and distributed algorithms. Submissions were encouraged in all areas of distributed systems and algorithms relevant to parallel computing, with emphasis on design and practice of distributed algorithms, analysis of the behaviour of distributed systems and algorithms, distributed fault-tolerance, distributed operating systems and databases, scalability, concurrency and performance in distributed systems, resource sharing and load balancing in distributed systems, distributed algorithms in telecommunications, distributed mobile computing, resource and service discovery, security in distributed systems, and standards and middleware for the distribution of parallel computations. Twenty papers were submitted in this topic. The subjects were varied, but a common theme of many of them is recovery, resource allocation, mutual exclusion, garbage collection and coordination. Other themes include load balancing, scheduling and consensus algorithms. Eight papers have been accepted.

These papers provide a breadth of information about concurrent, parallel, and distributed computing that is generally useful and interesting from a computer science perspective.

1995

Description/Abstract The best enterprises have both a compelling need pulling them forward and an innovative technological solution pushing them on. In high-performance computing, we have the need for increased computational power in many applications and the inevitable long-term solution is massive parallelism. In the short term, the relation between pull and push may seem unclear as novel algorithms and software are needed to support parallel computing.

This paper studies the problem of mapping streaming applications that can be modeled by a series-parallel graph, onto a 2-dimensional tiled CMP architecture. The objective of the mapping is to minimize the energy consumption, using dynamic voltage scaling techniques, while maintaining a given level of performance, reflected by the rate of processing the data streams. This mapping problem turns out to be NP-hard, but we identify simpler instances, whose optimal solution can be computed by a dynamic programming algorithm in polynomial time. Several heuristics are proposed to tackle the general problem, building upon the theoretical results. Finally, we assess the performance of the heuristics through comprehensive simulations using the StreamIt workflow suite and randomly generated series-parallel graphs, and various CMP grid sizes.

In this paper a survey on current trends in parallel computing has been studied that depicts all the aspects of parallel computing system. A large computational problem that can not be solved by a single CPU can be divided into a chunk of small enough subtasks which are processed simultaneously by a parallel computer. The parallel computer consists of parallel computing hardware, parallel computing model, software support for parallel programming. Parallel performance measurement parameters and parallel benchmarks are used to measure the performance of a parallel computing system. The hardware and the software are specially designed for parallel algorithm and programming. This paper explores all the aspects of parallel computing and its usefulness.